Raffaello Camoriano

Incremental semiparametric inverse dynamics learning

This paper presents a novel approach for incremental semiparametric inverse dynamics learning. In particular, we consider the mixture of two approaches: Parametric modeling based on rigid body dynamics equations and nonparametric modeling based on incremental kernel methods, with no prior information on the mechanical properties of the system. The result is an incremental semiparametric approach, leveraging the advantages of both the parametric and nonparametric models. We validate the proposed technique learning the dynamics of one arm of the iCub humanoid robot.

Online semi-parametric learning for inverse dynamics modeling

This paper presents a semi-parametric algorithm for online learning of a robot inverse dynamics model. It combines the strength of the parametric and non-parametric modeling. The former exploits the rigid body dynamics equation, while the latter exploits a suitable kernel function. We provide an extensive comparison with other methods from the literature using real data from the iCub humanoid robot. In doing so we also compare two different techniques, namely cross validation and marginal likelihood optimization, for estimating the hyperparameters of the kernel function.

Development and Analysis of a New Specialized Gripper Mechanism for Garment Handling

This paper reports ongoing work on the design of a new gripper for garments handling. The development of this device is part of the CloPeMa European Project creating a robot system for automated manipulation of clothing and other textile items. First, we analyze the specificity of the application determining the requirements for the design and functioning of the grasping system. Textiles do not have a stable shape and cannot be manipulated on the basis of a priori geometric knowledge. The necessary exploration of the material and the environment is performed with the help of tactile sensors embedded in the fingertips of the gripper, complementing the vision system of the robotic work cell. The chosen design solution is a simple mechanism able to perform adequately the grasping task and to permit exploratory finger motions. The kinematics and statics of the mechanism are outlined briefly and, in accord with initial experiments, used to validate the design.Copyright

Incremental robot learning of new objects with fixed update time

We consider object recognition in the context of lifelong learning, where a robotic agent learns to discriminate between a growing number of object classes as it accumulates experience about the environment. We propose an incremental variant of the Regularized Least Squares for Classification (RLSC) algorithm, and exploit its structure to seamlessly add new classes to the learned model. The presented algorithm addresses the problem of having an unbalanced proportion of training examples per class, which occurs when new objects are presented to the system for the first time. We evaluate our algorithm on both a machine learning benchmark dataset and two challenging object recognition tasks in a robotic setting. Empirical evidence shows that our approach achieves comparable or higher classification performance than its batch counterpart when classes are unbalanced, while being significantly faster.